Container Orchestration Research Articles

MCX Cloud-a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing.

.Significance: Despite the ample progress made toward faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers to widespread use among the broad user community.Aim: An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization.Approach: We have developed a configuration-free, in-browser 3D MC simulation platform—Monte Carlo eXtreme (MCX) Cloud—built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas.Results: The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using MCX over a dynamically expandable virtual cluster.Conclusion: MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.

Parked Vehicles Task Offloading in Edge Computing

The analytical research has recently indicated that the computational resources of Connected Autonomous Vehicles (CAVs) have been wasted since almost all vehicles spend over 95% of their time in parking lots. This paper presents a collaborative computing framework to efficiently offload online computational tasks to parked vehicles (PVs) during peak business hours. To maintain the service continuity, we advocate for integrating Kubernetes-based container orchestration to leverage its advanced features (e.g., auto-healing, load balancing, and security). We analytically formulate the task-offloading problem and then propose an intelligent meta-heuristic algorithm to dynamically deal with online heterogeneous demands. Additionally, we take a cumulative incentives model into account, where the PV owners are able to earn profit by sharing their computation resources. We also compare our algorithm with several existent heuristics on different sizes of the parking lot. Extensive simulation results show that our proposed computing framework significantly increases the possibility of accepting the online tasks and improves average task offloading cost by at least 40%. Besides, we quantify the PV availability by task acceptance ratios, which can be a critical criterion for network planners to achieve desired network service goals.

Container Technologies for ARM Architecture: A Comprehensive Survey of the State-of-the-Art

Container technology is becoming increasingly popular as an alternative to traditional virtual machines because it provides a faster, lighter, and more portable runtime environment for the applications. A container bundles the application and its binary code, libraries, and configuration files together while sharing the host operating system image. Accordingly, containers efficiently share resources and operate small micro-services, software programs, and even more extensive applications with less overhead than virtual machines. There are many container technologies available with Docker being the most popular and many technologies support multiple architectures, including the ARM architecture. Due to its energy efficiency and high-performance, which are crucial parameters in containerization, ARM architecture is becoming prevalent in container technologies. In this paper, we explore various container technologies that support ARM architecture and investigate the pros and cons of each technology. Moreover, we provide a comparative analysis of both container orchestrators and container runtimes that are most prominent competitors of Docker. We also consider security of container technologies with particular focus on the image scanning tools that supports ARM architecture. Our survey reveals that ARM technology is gaining popularity in containerization and almost all recent technologies support ARM architecture.

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.

Node-Based Horizontal Pod Autoscaler in KubeEdge-Based Edge Computing Infrastructure

KubeEdge (KE) is a container orchestration platform for deploying and managing containerized IoT applications in an edge computing environment based on Kubernetes. It is intended to be hosted at the edge and provides seamless cloud-edge coordination as well as an offline mode that allows the edge to function independently of the cloud. However, there are unreliable communication links between edge nodes in edge computing environments, implying that load balancing in an edge computing environment is not guaranteed while using KE. Furthermore, KE lacks Horizontal Pod Autoscaling (HPA), implying that KE cannot dynamically deploy new resources to efficiently handle increasing requests. Both of the aforementioned issues have a significant impact on the performance of the KE-based edge computing system, particularly when traffic volumes vary over time and geographical location. In this study, a node-based horizontal pod autoscaler (NHPA) is proposed to provide dynamical adjustment for the number of pods of individual nodes independently from each other in an edge computing environment where the traffic volume fluctuates over time and location, and the communication links between edge nodes are not stable. The proposed NHPA can dynamically adjust the number of pods depending on the incoming traffic at each node, which will improve the overall performance of the KubeEdge-based edge computing environment. In the KubeEdge-based edge computing environment, the experimental findings reveal that NHPA outperforms KE in terms of throughput and response time by a factor of about 3 and 25, respectively.

A secure cross‐organizational container deployment approach to enable ad hoc collaborations

SummaryWhen organizations need to collaborate urgently, for example, in the case of an emergency situation, it is needed to deploy software components into the different domains in order to allow crucial data to be exchanged. The ad hoc aspect is important as it does not allow the participating organizations to negotiate entire workflows and/or contracts upfront. To enable these ad hoc cross‐organizational collaborations, a container orchestration platform, like Kubernetes, can be used to quickly deploy pods of containers in a cross‐organizational overlay network, even fully automated. Although this is technically feasible, there may be a trust issue from the perspective of a participating organization when an external organization is capable of deploying any software inside its network domain. This concern is examined and resolved in this article, by proposing an extension to the existing deployment scheme used in vanilla Kubernetes. It allows the participating organizations to assess whether a suggested deployment conforms to the goal of the project and to maintain an overview of all activities related to a single collaboration. This intermediate step prevents an honest organization against potentially malicious behaviour of external entities, either the orchestrator and/or the other organizations, solving the aforementioned trust issue. Evaluation of the implemented prototype shows that a secure collaboration, which requires at most tens of containers, can be attained with sub‐second deployment overheads per container, apart from the required manual interventions for trust management purposes.

EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

A Fine-Grained Horizontal Scaling Method for Container-Based Cloud

The container scaling mechanism, or elastic scaling, means the cluster can be dynamically adjusted based on the workload. As a typical container orchestration tool in cloud computing, Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods in a replication controller, deployment, replication set, or stateful set based on observed CPU utilization. There are several concerns with the current HPA technology. The first concern is that it can easily lead to untimely scaling and insufficient scaling for burst traffic. The second is that the antijitter mechanism of HPA may cause an inadequate number of onetime scale-outs and, thus, the inability to satisfy subsequent service requests. The third concern is that the fixed data sampling time means that the time interval for data reporting is the same for average and high loads, leading to untimely and insufficient scaling at high load times. In this study, we propose a Double Threshold Horizontal Pod Autoscaler (DHPA) algorithm, which fine-grained divides the scale of events into three categories: scale-out, no scale, and scale-in. And then, on the scaling strength, we also employ two thresholds that are further subdivided into no scaling (antijitter), regular scaling, and fast scaling for each of the three cases. The DHPA algorithm determines the scaling strategy using the average of the growth rates of CPU utilization, and thus, different scheduling policies are adopted. We compare the DHPA with the HPA algorithm under different loads, including low, medium, and high. The experiments show that the DHPA algorithm has better antijitter and antiload characteristics in container increase and reduction while ensuring service and cluster security.

Telco Data Analytics using Open-Source Data Pipeline: Detailed Architecture and Technology Stack

Abstract: Open-source technology has influenced data analytics at each step from data storage to data analysis, and visualization. Open source for telco big data analytics enables sharp insights by enhancing problem discoverability and solution feasibility. This research paper talks about different technology stacks using open source for telco big data analytics that are used to deploy various tools including data collection, data storage, data processing, data analysis, and data visualization. This open source pipeline micro-services architecture built with modular technology stack and orchestrated by Kubernetes, can ingest data from multiple sources, process real-time data and provide business and network intelligence. Major idea of using open source technology in our architecture is to reduce cost and manage easily. Kubernetes is an industry adopted open source container orchestrator that offers fault-tolerance, application scaling, and load-balancing. The results can be displayed on the intuitive open source dashboard like Grafana for telecom operators. Our architecture is flexible and can be easily customized based on the telecommunication industry needs. Using the proposed architecture, the telecommunication sectors can get quick decision making with nearly 30% lower CapEX which is made possible using COTS hardware. Index Terms: Big data analytics, Data pipeline architecture, Open Source technologies, Real-time data processing, Faulttolerance, Load-balancing, Kubernetes, BDA, Open source dashboard

QoS-Based Service-Time Scheduling in the IoT-Edge Cloud.

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user’s QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

Distributed and Decentralized Orchestration of Containers on Edge Clouds

Distributed and Decentralized Orchestration of Containers on Edge Clouds

Leadership Hijacking in Docker Swarm and Its Consequences.

With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, which is often referred to as container infrastructures. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability and fault tolerance. Occasionally, discovered container escape vulnerabilities allow adversaries to execute code on the host operating system and operate within the cloud infrastructure. We show that Docker Swarm is currently not secured against misbehaving manager nodes. This allows a high impact, high probability privilege escalation attack, which we refer to as leadership hijacking, the possibility of which is neglected by the current cloud security literature. Cloud lateral movement and defense evasion payloads allow an adversary to leverage the Docker Swarm functionality to control each and every host in the underlying cluster. We demonstrate an end-to-end attack, in which an adversary with access to an application running on the cluster achieves full control of the cluster. To reduce the probability of a successful high impact attack, container orchestration infrastructures must reduce the trust level of participating nodes and, in particular, incorporate adversary immune leader election algorithms.

Benchmarking geospatial database on Kubernetes cluster

Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

Centralised Cleanup Service for Kubernetes and Cloud Resources

The architectural style of developing a software application using loosely coupled and highly cohesive services can be termed as microservices architecture. The microservices allow agile software development and enable businesses to build and deliver applications quickly. To achieve the benefits of microservices, an underlying infrastructure that supports them must exist. This includes CI/CD pipelines, execution environments like virtual machines and containers, logging and monitoring, communication mechanisms, and so on. Containers are lightweight, enable multiple execution environments to exist on a single operating system instance, and provide isolation. Container Orchestration Engines such as Docker swarm or Kubernetes automate deployment, scaling, fault tolerance, and container networking. Many organizations use containers to spawn resources in public or private clouds. Different engineering teams perform various kinds of tests by bundling the test code and dependencies into containers. However, cleaning up these containers is necessary for the efficient utilization of hardware resources. This paper discusses the need and benefits of a centralized cleanup service for Kubernetes and cloud resources. It analyzes the value additions this service can bring to the software development process of large organizations.

Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes

Presently, the cloud computing environment attracts many application developers to deploy their web applications on cloud data centers. Kubernetes, a well-known container orchestration for deploying web applications on cloud systems, offers an automatic scaling feature to meet clients’ ever-changing demands with the reactive approach. This paper proposes a system architecture based on Kubernetes with a proactive custom autoscaler using a deep neural network model to handle the workload during run time dynamically. The proposed system architecture is designed based on the Monitor–Analyze–Plan–Execute (MAPE) loop. The main contribution of this paper is the proactive custom autoscaler, which focuses on the analysis and planning phases. In analysis phase, Bidirectional Long Short-term Memory (Bi-LSTM) is applied to predict the number of HTTP workloads in the future. In the planning phase, a cooling-down time period is implemented to mitigate the oscillation problem. In addition, a resource removal strategy is proposed to remove a part of the resources when the workload decreases, so that the autoscaler can handle it faster when the burst of workload happens. Through experiments with two different realistic workloads, the Bi-LSTM model achieves better accuracy not only than the Long Short-Term Memory model but also than the state-of-the-art statistical auto-regression integrated moving average model in terms of short- and long-term forecasting. Moreover, it offers 530 to 600 times faster prediction speed than ARIMA models with different workloads. Furthermore, as compared to the LSTM model, the Bi-LSTM model performs better in terms of resource provision accuracy and elastic speedup. Finally, it is shown that the proposed proactive custom autoscaler outperforms the default horizontal pod autoscaler (HPA) of the Kubernetes in terms of accuracy and speed when provisioning and de-provisioning resources.

Multi-Cloud Adoption Challenges for the Cloud-Native Era

With the ready availability of appropriate technologies and tools for crafting hybrid clouds, the move towards employing multiple clouds for hosting and running various business workloads is garnering subtle attention. The concept of cloud-native computing is gaining prominence with the faster proliferation of microservices and containers. The faster stability and maturity of container orchestration platforms also greatly contribute towards the cloud-native era. This paper guarantees the following contributions: 1) It describes the key motivations for multi-cloud concept and implementations. 2) It also highlights various key drivers of the multi-cloud paradigm. 3) It presents a brew of challenges that are likely to occur while setting up multi-cloud. 4) It elaborates the proven and potential solution approaches to solve the challenges. The technology-inspired and tool-enabled solution approaches significantly simplify and speed up the adoption of the fast-emerging and evolving multi-cloud concept in the cloud-native era.

마이크로서비스의 특징을 반영한 컨테이너 오케스트레이션 프레임워크 배치 기법

본 논문에서 마이크로서비스(microservice) 구조의 어플리케이션을 구성할 때, 마이크로서비스의 약결합(loosely-coupling) 특성을 반영한 배치 명세를 제안한다. 또한, 제안된 배치 명세 기반의 마이크로서비스 어플리케이션을 컨테이너 오케스트레이션 프레임워크(container orchestration framework)에 배치하는 방안을 제안한다. 제안된 기법을 Kubernetes의 기본 템플릿을 활용한 배치와의 차이점을 비교하고, 그 성능을 측정하기 위하여 시뮬레이션을 통하여 마이크로서비스 응답 지연시간을 측정하였다. 그 결과, Kubernetes의 기본 템플릿을 활용한 경우는 마이크로서비스 구조를 반영하지 않아 무작위로 배치되는 경향을 보였으나 제안된 기법의 경우 응답 지연시간을 줄이는 적절한 위치에 마이크로서비스가 배치됨을 확인하였다. 그리고 기존의 방법에 비해 평균 응답 지연시간이 약 1.95ms 감소하였다.

Container Orchestration: Experiences with Container Orchestration Platforms Like Docker Swarm, Kubernetes, and Nomad, Focusing on Scalability and Security Improvements

Container Orchestration: Experiences with Container Orchestration Platforms Like Docker Swarm, Kubernetes, and Nomad, Focusing on Scalability and Security Improvements

Machine Learning-Based Scaling Management for Kubernetes Edge Clusters

Kubernetes, the container orchestrator for cloud-deployed applications, offers automatic scaling for the application provider in order to meet the ever-changing intensity of processing demand. This auto-scaling feature can be customized with a parameter set, but those management parameters are static while incoming Web request dynamics often change, not to mention the fact that scaling decisions are inherently reactive, instead of being proactive. We set the ultimate goal of making cloud-based applications' management easier and more effective. We propose a Kubernetes scaling engine that makes the auto-scaling decisions apt for handling the actual variability of incoming requests. In this engine various machine learning forecast methods compete with each other via a short-term evaluation loop in order to always give the lead to the method that suits best the actual request dynamics. We also introduce a compact management parameter for the cloud-tenant application provider to easily set their sweet spot in the resource over-provisioning vs. SLA violation trade-off. We motivate our scaling solution with analytical modeling and evaluation of the current Kubernetes behavior. The multi-forecast scaling engine and the proposed management parameter are evaluated both in simulations and with measurements on our collected Web traces to show the improved quality of fitting provisioned resources to service demand. We find that with just a few, but fundamentally different, and competing forecast methods, our auto-scaler engine, implemented in Kubernetes, results in significantly fewer lost requests with just slightly more provisioned resources compared to the default baseline.